The single neuronSingle neuron considered in Chap. 10 can be used as an introductory tool for understanding the working principles of neural networks.Neural network However, it is not sufficient enough to solve complex machine learning problems. Therefore, we should form a more powerful structure called multilayer neural networks,Neural network generally called neural networks.Neural network We will consider this structure in this chapter. To do so, we will start with the background information on neural networks.Neural network Here, we will define structure of the neural networkNeural network as well as deep and shallow neural networks.Neural network We will also form the fully connected neural networkNeural network in line with the background information. Then, we will show how to implement a neural networkNeural network in Keras. Here, we will cover the layer structure and how to use it in forming the network. Next, we will focus on training the neural network.Neural network To do so, we will briefly introduce the theory behind the backpropagationBackpropagation algorithm. We will also explore training steps under Keras. Afterward, we will form a classifier and regressor via neural networks.Neural network Finally, we will consider real-life applications introduced in previous chapters now from the neural networksNeural network perspective.

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Multilayer Neural Networks

  • Cem Ünsalan,
  • Berkan Höke,
  • Eren Atmaca

摘要

The single neuronSingle neuron considered in Chap. 10 can be used as an introductory tool for understanding the working principles of neural networks.Neural network However, it is not sufficient enough to solve complex machine learning problems. Therefore, we should form a more powerful structure called multilayer neural networks,Neural network generally called neural networks.Neural network We will consider this structure in this chapter. To do so, we will start with the background information on neural networks.Neural network Here, we will define structure of the neural networkNeural network as well as deep and shallow neural networks.Neural network We will also form the fully connected neural networkNeural network in line with the background information. Then, we will show how to implement a neural networkNeural network in Keras. Here, we will cover the layer structure and how to use it in forming the network. Next, we will focus on training the neural network.Neural network To do so, we will briefly introduce the theory behind the backpropagationBackpropagation algorithm. We will also explore training steps under Keras. Afterward, we will form a classifier and regressor via neural networks.Neural network Finally, we will consider real-life applications introduced in previous chapters now from the neural networksNeural network perspective.